IDEAS home Printed from https://ideas.repec.org/a/wly/quante/v17y2026i3p623-669.html

Binary choice under asymmetric loss in a data‐rich environment: Theory and an application to algorithmic fairness

Author

Listed:
  • Andrii Babii
  • Xi Chen
  • Eric Ghysels
  • Rohit Kumar

Abstract

We study the binary choice problem in a data‐rich environment with asymmetric loss functions. The econometrics literature covers nonparametric binary choice problems but does not offer computationally attractive solutions in data‐rich environments. The machine learning literature has many algorithms but is focused mostly on loss functions that are independent of covariates. We show that theoretically valid decisions on binary outcomes with general loss functions can be achieved via a very simple loss‐based reweighting of logistic regression or state‐of‐the‐art machine learning techniques. We apply our analysis to algorithmic fairness in pretrial detentions.

Suggested Citation

  • Andrii Babii & Xi Chen & Eric Ghysels & Rohit Kumar, 2026. "Binary choice under asymmetric loss in a data‐rich environment: Theory and an application to algorithmic fairness," Quantitative Economics, Econometric Society, vol. 17(3), pages 623-669, July.
    • Handle: RePEc:wly:quante:v:17:y:2026:i:3:p:623-669
    DOI: 10.3982/QE2385
    as

    Download full text from publisher

    File URL: https://doi.org/10.3982/QE2385
    Download Restriction: no
    File URL: https://libkey.io/10.3982/QE2385?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:quante:v:17:y:2026:i:3:p:623-669. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/essssea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.